There is a number of recent approaches to trace semantic change via distributional methods. This includes mainly (i), semantic similarity models assuming one sense for each word and then measuring its spatial displacement by a similarity metric (such as cosine) in a semantic vector space [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]} and (ii), word sense induction models (WSI) inferring for each word a probability distribution over different word senses (or topics) in turn modeled as a distribution over words [8]}, [9]}, [10]}, [11]}, [12]}, [13]}.
